DahnResearch

Privacy Statement

Since being retired I enjoy the freedom to read many interesting papers and posts for which I didn‘t have time while being on the job. On this page I collect references to contributions from many authors which I find most interesting and thought-provoking. Having a document listed on this page doesn‘t necessarily mean that I share the views presented by the authors. The selection on this page is admittedly very subjective. It is determined by my interests, by my level of experience in the different fields and by accident. While this page comes as a recommendation for others it serves at least to the same extent as an aide-memoire for myself to keep thoughts I am likely to come back to in the future. Naturally, this page is dynamic. New posts will be announced on Twitter.

eLearning

Maurice de Hond: The Challenge of Personalized Learning in Schools. 2018, Personalization in practice Rule, Birmingham, Zuniga et all.: Ten simple rules for writing and sharing computational analyses in Jupyter Notebooks, :PLOS Computational Biology (July 2019) Nial Slater, Joel Mullan: Learning Analytics and Student Success - Assessing the Evidence; JISC Briefing, January 2017. One of the few papers providing data-backed evidence for potential of big data methods for learning. T. Farrel Frey, A. Mikroyannidis, H. Alani: „We‘re Seeking Relevance“: Qualitative Perspectives on the Impact of Learning Analytics on teaching and Learning, Conference Paper, September 2017. Indicates different information needs for teachers in STEM- and non-STEM domains.

Artificial Intelligence

Jessy Lin: Rethinking human-AI interaction, 2020 Alex Graves, Greg Wayne, Malcolm Reynolds et al.: Hybrid Computing Using a Neural Network with Dynamic Esternal Memory; Natrue 538, pp. 471-476, October 2016. This paper introduces Differentiable Neural Computers (DNC) - extended neural networks that can ldemonstrably earn logical reasoning. Francois Chollet: The Limitations of Deep Learning; the Keras Blog, July 2017 The author argues that pattern recognition and reasoning are complementary.